1) Accuracy
Deliberate steps should be taken to minimize the chance of making an error and maximize the chance of catching errors when errors inevitably occur.
2) Computational reproducibility
Computational reproducibility should be embraced to improve accuracy, promote transparency, and prove the quality of analytical work.
3) Human interpretability
Code should be written so humans can easily understand what’s happening—even if it occasionally sacrifices machine performance.
4) Portability
Analyses should be designed so strangers can understand each and every step without additional instruction or inquiry from the original analyst.
5) Accessibility
Research and data are non-rival and non-exclusive. They are public goods that should be widely and easily shared. Decisions about tools, methods, data, and language during the research process should be made in ways that promote the ability of anyone and everyone to access an analysis.
6) Efficiency
Analysts should seek to make all parts of the research process more efficient with clear communication, by adopting best practices, and by managing computation.
Principles
- Accuracy
- Computational reproducibility
- Human interpretability
- Portability
- Accessibility
- Efficiency
Text editor/IDE
- R == free, open source programming language
- RStudio == for-profit company and Itegrated Development Environment (IDE)
RStudio
The R console
Computational Reproducibility
- Replication: the recreation of findings across repeated studies, is a cornerstone of science.
- Reproducibility: the ability to access data, source code, tools, and documentation and recreate all calculations, visualizations, and artifacts of an analysis
- Computational reproducibility should be the minimum standard for computational social sciences and statistical programming
Script
- A plain text document that contains code and comments
- Map to the answer
.R and .Rmd
Coding style
“Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.” ~ Hadley Wickham
- CamelCase
- camelCase
- snake_case
tidyverse style guide
R Packages
Collections of R, C, C++, and FORTRAN code that expand the functionality of R.
CRAN
The Comprehensive R Archive Network was introduced in 1997.
Repository of popular R packages with basic standards and quality control.
tidyverse
Comprehensive set of tools for data science
Core: ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats
tidyverse
Free text by Hadley Wickham and Garrett Grolemund
Installing and loading packages
Data structures
Scalars (do not exist in R)
Vectors
## [1] 1 2 3 4 5
Matrices
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
Data frames, multidimensional arrays
## # A tibble: 4 x 4
## name awake brainwt bodywt
## <chr> <dbl> <dbl> <dbl>
## 1 Cheetah 11.9 NA 50
## 2 Owl monkey 7 0.0155 0.48
## 3 Mountain beaver 9.6 NA 1.35
## 4 Greater short-tailed shrew 9.1 0.00029 0.019
Data types
Character
## [1] "a" "b" "c" "d" "e"
Numeric
## [1] 1 2 3 4 5
Logical
## [1] TRUE TRUE FALSE TRUE FALSE
Factor
## [1] good ok bad ok ok
## Levels: good ok bad
Missing values
A great strength of R!
NA is R’s encoding for missing values.
Missing values are contagious.
## [1] NA
Assignment
R can hold many different objects at the same time. This requires assignment.
<-
## [1] 4
## [1] 4
Functions
Arguments by position
## [1] 2.5
Arguments by name
## [1] 2.5
Function documentation ?mean
Custom functions
Rule of three: never program something three or more times
## [1] "odd!" "even!" "odd!" "even!" "odd!" "even!" "odd!" "even!"
## [9] "odd!" "even!"
Tests
What will it take to convince you that your code is correct?
- Assign monthly observations to fiscal years
- Are there 12 months per year?
- Link observations from 2017 to observations from 2018.
- Do non-matching variables that shouldn’t change change?
- Tax calculator
Are values that must be positive non-positive?
Write the test first!
Each time you encounter a bug, write a test that will convince you the bug no longer exists.
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